{ "meta_info": { "case_id": "BCIC2020-3_04.edf", "bench_subset": "NeuroBench-Core", "difficult": 1.5, "original_dataset": "BCIC2020-3" }, "agent_input": { "data_path": "data/core/BCIC2020-3_04.edf", "instruction": "Please select the specified central EEG channel C3, compute the 49-51Hz narrowband power before notch filtering, apply a 60Hz FIR notch filter, compute the 49-51Hz narrowband power after notch filtering, calculate the ratio post_notch_power / pre_notch_power, and report both the ratio and whether line-noise interference was successfully suppressed. Define successful suppression as a ratio strictly below 0.5." }, "eval_config": { "parser_prompt": "### ROLE\nYou are a precise JSON Extraction Engine for neurophysiological data. Your sole task is to convert natural language reports into structured JSON data.\n\n### TASK\nExtract two outputs from the agent report:\n1) suppression ratio defined as post_notch_power / pre_notch_power\n2) whether line-noise interference was successfully suppressed\n\n### STRICT CONSTRAINTS (MANDATORY)\n1. Output ONLY a valid JSON object.\n2. DO NOT include Markdown code blocks.\n3. DO NOT include any conversational text or explanations.\n4. Keys in JSON must be EXACTLY \"suppression_ratio\" and \"suppressed\".\n5. \"suppression_ratio\" must be a FLOAT or null.\n6. \"suppressed\" must be either true, false, or null.\n\n### OUTPUT TEMPLATE\n{\"suppression_ratio\": , \"suppressed\": }", "metrics": [ { "metric_id": "suppression_ratio_accuracy", "type": "numeric_check", "target_key": "suppression_ratio", "weight": 70, "params": { "gt_value": 1.000519678602253, "tolerance": 0.1000519678602253 } }, { "metric_id": "suppression_success_accuracy", "type": "categorical_check", "target_key": "suppressed", "weight": 30, "params": { "gt_value": false } } ] } }